Welcome to DAGitty!

What is this?

DAGitty is a browser-based environment for creating, editing, and analyzing
causal models (also known as directed acyclic graphs or causal Bayesian networks).
The focus is on the use of causal diagrams for minimizing bias in empirical
studies in epidemiology and other disciplines. For background information, see
the "learn" page.

How can I get help?

If you encounter any problems using DAGitty, or would like to have a certain
feature implemented, please write to "johannes {dot} textor {at} gmx {dot}
de". Your feedback and bug reports are very welcome and contribute to
making DAGitty a better experience for everyone.
Past contributors are acknowledged in the manual.

Is it free?

Because the main purposoe of DAGitty is facilitating the use of causal models
in empirical studies, it is and will always be Free software (both
"free as in beer" and "free as in speech"). You can copy, redistribute, and
modify it under the terms of the
GNU general public license.
Enjoy!

How can I cite DAGitty?

If you use DAGitty in your scientific work, please consider
citing us:

News on Twitter

Changelog

Version 2.3 has been released! The most notable new feature:
instrumental variables.

2014-10-30

Version 2.2 has been released!

2014-10-05

Version 2.2 is forthcoming and now available as the
Development version. This version features
a new, SEM-like diagram drawing style and the ability to share your DAGs
online.

2014-04-14

At "daggity.net/learn", I am building some interactive tutorials
using the forthcoming version 2.1 of DAGitty. That version will
be embeddable into HTML pages, which will make it easy to include
interactive DAG drawings into just about any webpage. Check it out!
The first examples include an implementation of the "Simpson Machine"
and an interactive version of a tutorial text on d-separation.